Summary of Test-time Augmentation For Traveling Salesperson Problem, by Ryo Ishiyama et al.
Test-Time Augmentation for Traveling Salesperson Problem
by Ryo Ishiyama, Takahiro Shirakawa, Seiichi Uchida, Shinnosuke Matsuo
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Test-Time Augmentation (TTA) technique addresses combinatorial optimization problems like the Traveling Salesperson Problem by efficiently learning graph structures in deep learning models. TTA interprets node index permutations as an augmentation scheme, enabling shorter solution obtention compared to latest models. The results demonstrate increased probability of finding exact solutions as augmentation size increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We propose a new technique called Test-Time Augmentation (TTA) that helps solve big optimization problems like the Traveling Salesperson Problem. This method uses special deep learning models that can efficiently learn graph structures. TTA is like a “shake-up” for these models, where we randomly switch around node indices to help find better solutions. Our results show that this technique finds shorter and more accurate solutions than other methods. |
Keywords
» Artificial intelligence » Deep learning » Optimization » Probability
